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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/69190

    Title: Estimation of exponential regerssion parameters using binary data
    Authors: Cheng, K.F.;Wu, Jong-wuu
    Contributors: 淡江大學統計學系
    Keywords: Exponential regression;least square method;binary data;maximum likelihood estimate
    Date: 1992-08
    Issue Date: 2013-05-31 11:38:36 (UTC+8)
    Publisher: Philadelphia: Taylor & Francis Inc.
    Abstract: Exponential regression model is important in analyzing data from heterogeneous populations. In this paper we propose a simple method to estimate the regression parameters using binary data. Under certain design distributions, including ellipticaily symmetric distributions, for the explanatory variables, the estimators are shown to be consistent and asymptotically normal when sample size is large. For finite samples, the new estimates were shown to behave reasonably well. They are competitive with the maximum likelihood estimates and more importantly, according to our simulation results, the cost of CPU time for computing new estimates is only 1/7 of that required for computing the usual maximum likelihood estimates. We expect the savings in CPU time would be more dramatic with larger dimension of the regression parameter space.
    Relation: Communications in Statistics: Theory and Methods 21(8), pp.2203-2214
    DOI: 10.1080/03610929208830907
    Appears in Collections:[統計學系暨研究所] 期刊論文

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